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Many modern neural networks are trained in an over-parameterized regime where the parameters of the model exceed the size of the training dataset. Due to their over-parameterized nature these models in principle have the capacity to (over)fit any set of labels including pure noise. Despite this high fitting capacity, somewhat paradoxically, models trained via first-order methods (often with early stopping) continue to predict well on yet unseen test data.
The role of image quality assessment in tasks such as (i) pan sharpening (PS) (i.e. merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image) and (ii) super-resolution (SR) has not been researched extensively from the natural scene statistics (NSS) perspective. For instance, even though there are several well-known measures that quantify the quality of PS and SR images, there has been little work done on analyzing the statistics of PS and SR images and associated distortions.
Soft electronic devices that can acquire vital signs from the human body represent an important trend for healthcare. Combined strategies of materials design and advanced microfabrication allow the integration of a variety of components and devices on a stretchable platform, resulting in functional systems with minimal constraints on the human body. In this presentation, I will demonstrate a wearable multichannel patch that can sense a collection of signals from the human skin in a wireless mode.